02_CE211_Analysis

Load Libraries

require(tidyverse)

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## v readr   2.0.2     v forcats 0.5.1

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require(lubridate)

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require(stringr)
require(readxl)

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require(arsenal)

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require(gtsummary)

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require(plotly)

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Load From file

needs <- read.csv("../Data/Cleaned/01_Needs.csv")
names(needs) <- toupper(names(needs))
referrals <- read.csv("../Data/Cleaned/01_referrals.csv")
names(referrals) <- toupper(names(referrals))

Type Covert

needs <- needs %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), LANGUAGE = as.factor(LANGUAGE)
                          ,MILITARY_STATUS = as.factor(MILITARY_STATUS)
                          ,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
                          ,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
                          ,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE)
                          );

referrals <- referrals %>% mutate(AGE = as.factor(AGE), Gender = as.factor(GENDER), Language = as.factor(LANGUAGE)
                          ,MILITARY_STATUS = as.factor(MILITARY_STATUS)
                          ,DISABILITY_STATUS = as.factor(DISABILITY_STATUS)
                          ,DISABILITY_TYPE = as.factor(DISABILITY_TYPE)
                          ,HEALTH_INSURANCE = as.factor(HEALTH_INSURANCE))

Generate Stats

needs %>% select(NEED) %>% tbl_summary()

Characteristic	N = 26,0951
NEED
Arts Culture and Recreation	2 (<0.1%)
Clothing/Personal/Household Needs	112 (0.4%)
Disaster Services	181 (0.7%)
Education	10 (<0.1%)
Employment	241 (0.9%)
Food/Meals	737 (2.8%)
Health Care	285 (1.1%)
Housing	10,915 (42%)
Income Support/Assistance	285 (1.1%)
Individual Family and Community Support	285 (1.1%)
Information Services	11,931 (46%)
Legal Consumer and Public Safety Services	283 (1.1%)
Mental Health/Addictions	493 (1.9%)
Other Government/Economic Services	39 (0.1%)
Transportation	123 (0.5%)
Utility Assistance	77 (0.3%)
Volunteers/Donations	96 (0.4%)
1 n (%)

NEED : Replace non housing and non information services with others

needs <- needs %>% mutate(NEED = if_else(NEED != 'Housing' & NEED != 'Information Services','Others',NEED))
needs$NEED <- as.factor(needs$NEED)

Generate Stats

needs %>% select(CE_SCREENED) %>% tbl_summary()

Characteristic	N = 26,0951
CE_SCREENED
DV Referral	920 (3.5%)
Literally Homeless	19,621 (75%)
Precariously Housed	359 (1.4%)
Risk of Homelessness	5,056 (19%)
ROI Declined	100 (0.4%)
Unknown	39
1 n (%)

needs %>% select(NEED,CE_SCREENED) %>% tbl_summary(by = NEED )

Characteristic	Housing, N = 10,9151	Information Services, N = 11,9311	Others, N = 3,2491
CE_SCREENED
DV Referral	581 (5.3%)	85 (0.7%)	254 (7.8%)
Literally Homeless	8,077 (74%)	9,304 (78%)	2,240 (69%)
Precariously Housed	124 (1.1%)	178 (1.5%)	57 (1.8%)
Risk of Homelessness	2,064 (19%)	2,328 (20%)	664 (20%)
ROI Declined	48 (0.4%)	24 (0.2%)	28 (0.9%)
Unknown	21	12	6
1 n (%)

needs %>% select(NEED = NEED,NEED_1 = NEED) %>% tbl_summary(by = NEED)

Characteristic	Housing, N = 10,9151	Information Services, N = 11,9311	Others, N = 3,2491
NEED_1
Housing	10,915 (100%)	0 (0%)	0 (0%)
Information Services	0 (0%)	11,931 (100%)	0 (0%)
Others	0 (0%)	0 (0%)	3,249 (100%)
1 n (%)

Q1 —-

1. Frequency- needs (% of clients indicating each need)

tab1 <- tableby(~NEED,data = needs %>% select(NEED,CONTACT_NUMBER) %>% distinct())
q1.a <- as.data.frame(tab1)
summary(tab1)

## 
## 
## |                                       | Overall (N=16696) |
## |:--------------------------------------|:-----------------:|
## |**NEED**                               |                   |
## |   Housing              |   6946 (41.6%)    |
## |   Information Services |   8840 (52.9%)    |
## |   Others               |    910 (5.5%)     |

needs %>% select(NEED,CONTACT_NUMBER) %>% distinct() %>% select(NEED) %>% tbl_summary()

Characteristic	N = 16,6961
NEED
Housing	6,946 (42%)
Information Services	8,840 (53%)
Others	910 (5.5%)
1 n (%)

q1.a.plt <- needs %>%select(NEED,CONTACT_NUMBER) %>% distinct() %>% group_by(NEED) %>% summarise(count=n())
fig <- plot_ly(data = q1.a.plt,x=~NEED,y=~count,type = 'bar')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')
fig

2. Frequency- number of needs per client

q1.b <- needs %>% select(CONTACT_NUMBER,NEED) %>% distinct() %>% group_by(CONTACT_NUMBER) %>% summarise(NUMBER_OF_NEEDS = n())
q1.b_2 <- q1.b %>% group_by(NUMBER_OF_NEEDS) %>% summarise(NUMBER_OF_CLIENTS = n()) 
q1.b_2

## # A tibble: 3 x 2
##   NUMBER_OF_NEEDS NUMBER_OF_CLIENTS
##             <int>             <int>
## 1               1             12666
## 2               2              1790
## 3               3               150

3. Frequency- CE Screened (all categories) filtered by Need=Housing
   • if Contact Number has Housing and Information Services Need, include in this frequency

q1.c <- needs %>% filter(NEED == 'Housing' | NEED == 'Information Services') %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)
q1.c %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,label %in% c('Housing','Information Services'))

## 17 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.

Characteristic	DV Referral, N = 4101	Literally Homeless, N = 12,7341	Precariously Housed, N = 1771	Risk of Homelessness, N = 2,6441	ROI Declined, N = 481
Housing	366 (89%)	5,517 (43%)	62 (35%)	1,112 (42%)	32 (67%)
Information Services	44 (11%)	7,217 (57%)	115 (65%)	1,532 (58%)	16 (33%)
1 n (%)

4. Frequency- CE Screened (all categories) filtered by Need=Information Services -if Contact Number has Housing and Information Services Need, exclude from this frequency

q1.d <- needs %>% filter(!(CONTACT_NUMBER %in% q1.c$CONTACT_NUMBER)) %>% distinct(CONTACT_NUMBER,CE_SCREENED,NEED)

q1.d %>% select(CE_SCREENED,NEED) %>% tbl_summary(by = CE_SCREENED,missing = "no") %>% modify_table_body(filter,!(label %in% c('Housing','Information Services')))

## 3 observations missing `CE_SCREENED` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `CE_SCREENED` column before passing to `tbl_summary()`.

Characteristic	DV Referral, N = 241	Literally Homeless, N = 1401	Precariously Housed, N = 21	Risk of Homelessness, N = 261	ROI Declined, N = 21
NEED
Others	24 (100%)	140 (100%)	2 (100%)	26 (100%)	2 (100%)
1 n (%)

5. Crosstab: CE Screened (all categories) filtered by Need=Housing x Gender

q1.e <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.e %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")

## 131 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.

Characteristic	Female, N = 4,3341	Male, N = 2,6061	Refused to Disclose, N = 71	Transgender, N = 231
CE_SCREENED
DV Referral	307 (7.1%)	39 (1.5%)	1 (14%)	0 (0%)
Literally Homeless	3,148 (73%)	2,273 (87%)	5 (71%)	17 (74%)
Precariously Housed	46 (1.1%)	10 (0.4%)	0 (0%)	0 (0%)
Risk of Homelessness	804 (19%)	274 (11%)	1 (14%)	6 (26%)
ROI Declined	21 (0.5%)	9 (0.3%)	0 (0%)	0 (0%)
1 n (%)

#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.e <- as.data.frame(tbl_by)

6. Crosstab: CE Screened (all categories) filtered by Need=Information Services x Gender

q1.f <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, CE_SCREENED, GENDER) %>% distinct()
q1.f %>% select(CE_SCREENED,GENDER) %>% tbl_summary(by = GENDER,missing = "no")

## 190 observations missing `GENDER` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `GENDER` column before passing to `tbl_summary()`.

Characteristic	Female, N = 4,9081	Male, N = 3,7821	Refused to Disclose, N = 181	Transgender, N = 311
CE_SCREENED
DV Referral	35 (0.7%)	5 (0.1%)	0 (0%)	0 (0%)
Literally Homeless	3,706 (76%)	3,326 (88%)	11 (61%)	26 (84%)
Precariously Housed	70 (1.4%)	27 (0.7%)	0 (0%)	1 (3.2%)
Risk of Homelessness	1,088 (22%)	415 (11%)	7 (39%)	4 (13%)
ROI Declined	7 (0.1%)	8 (0.2%)	0 (0%)	0 (0%)
1 n (%)

#tbl_by <- tableby(GENDER ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
#summary(tbl_by)
#q1.f <- as.data.frame(tbl_by)

7. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status

q1.g <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.g %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")

## 156 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.

Characteristic	No, N = 4,7021	Not Collected, N = 661	Refused to Disclose, N = 171	Yes, N = 2,1601
CE_SCREENED
DV Referral	243 (5.2%)	6 (9.1%)	6 (35%)	78 (3.6%)
Literally Homeless	3,614 (77%)	53 (80%)	10 (59%)	1,759 (81%)
Precariously Housed	38 (0.8%)	0 (0%)	0 (0%)	16 (0.7%)
Risk of Homelessness	781 (17%)	6 (9.1%)	0 (0%)	295 (14%)
ROI Declined	17 (0.4%)	1 (1.5%)	1 (5.9%)	11 (0.5%)
1 n (%)

#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
#summary(tbl_by)
#q1.g <- as.data.frame(tbl_by)

8. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status

q1.h <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, DISABILITY_STATUS, CE_SCREENED) %>% distinct()
q1.h %>% select(DISABILITY_STATUS,CE_SCREENED) %>% tbl_summary(by = DISABILITY_STATUS,missing = "no")

## 196 observations missing `DISABILITY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `DISABILITY_STATUS` column before passing to `tbl_summary()`.

Characteristic	No, N = 5,9181	Not Collected, N = 3711	Refused to Disclose, N = 241	Yes, N = 2,4201
CE_SCREENED
DV Referral	29 (0.5%)	0 (0%)	0 (0%)	10 (0.4%)
Literally Homeless	4,719 (80%)	357 (96%)	22 (92%)	1,967 (81%)
Precariously Housed	71 (1.2%)	2 (0.5%)	0 (0%)	23 (1.0%)
Risk of Homelessness	1,086 (18%)	11 (3.0%)	1 (4.2%)	416 (17%)
ROI Declined	10 (0.2%)	1 (0.3%)	1 (4.2%)	3 (0.1%)
1 n (%)

#tbl_by <- tableby(DISABILITY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.h <- as.data.frame(tbl_by)

1. Crosstab: CE Screened (all categories) filtered by Need=Housing x MILITARY_STATUS

q1.i <- needs %>% filter(NEED == 'Housing') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.i %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")

## 207 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.

Characteristic	Active, N = 31	No, N = 6,5011	Not Collected, N = 331	Refused to Disclose, N = 131	Veteran, N = 3441
CE_SCREENED
DV Referral	1 (33%)	308 (4.7%)	3 (9.1%)	5 (38%)	9 (2.6%)
Literally Homeless	2 (67%)	5,072 (78%)	24 (73%)	8 (62%)	288 (84%)
Precariously Housed	0 (0%)	50 (0.8%)	2 (6.1%)	0 (0%)	2 (0.6%)
Risk of Homelessness	0 (0%)	1,032 (16%)	3 (9.1%)	0 (0%)	45 (13%)
ROI Declined	0 (0%)	29 (0.4%)	1 (3.0%)	0 (0%)	0 (0%)
1 n (%)

#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Housing'))
##summary(tbl_by)
#q1.i <- as.data.frame(tbl_by)

10. Crosstab: CE Screened (all categories) filtered by Need=Housing x Disability Status

q1.j <- needs %>% filter(NEED == 'Information Services') %>% select(CONTACT_NUMBER, MILITARY_STATUS, CE_SCREENED) %>% distinct()
q1.j %>% select(MILITARY_STATUS,CE_SCREENED) %>% tbl_summary(by = MILITARY_STATUS,missing = "no")

## 317 observations missing `MILITARY_STATUS` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `MILITARY_STATUS` column before passing to `tbl_summary()`.

Characteristic	Active, N = 141	No, N = 8,0921	Not Collected, N = 251	Refused to Disclose, N = 121	Veteran, N = 4691
CE_SCREENED
DV Referral	0 (0%)	36 (0.4%)	0 (0%)	0 (0%)	3 (0.6%)
Literally Homeless	11 (79%)	6,514 (81%)	14 (56%)	8 (67%)	407 (87%)
Precariously Housed	1 (7.1%)	87 (1.1%)	2 (8.0%)	0 (0%)	3 (0.6%)
Risk of Homelessness	2 (14%)	1,439 (18%)	8 (32%)	3 (25%)	56 (12%)
ROI Declined	0 (0%)	13 (0.2%)	1 (4.0%)	1 (8.3%)	0 (0%)
1 n (%)

#tbl_by <- tableby(MILITARY_STATUS ~ CE_SCREENED ,data = needs %>% filter(NEED == 'Information Services'))
##summary(tbl_by)
#q1.j <- as.data.frame(tbl_by)

Q2 -

q2 <- needs %>% select(CONTACT_NUMBER,GENDER,DISABILITY_STATUS,MILITARY_STATUS,AGE,NEED,REFERRAL) %>% distinct()
q2 %>% select(-c(CONTACT_NUMBER)) %>% tbl_summary(by = REFERRAL,missing = "no")

## 113 observations missing `REFERRAL` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `REFERRAL` column before passing to `tbl_summary()`.

Characteristic	2020 Census, N = 31	211.org, N = 21	411 Directory Assistance, N = 41	AARP, N = 21	Agape Christian Counseling, N = 21	American Cancer Society, N = 21	American Red Cross - Charlotte Metro Chapter, N = 181	Angel House Maternity Home, N = 111	Area Agency on Aging - Centralina, N = 61	Asheville-Buncombe Community Christian Ministry, N = 391	Autism Society of North Carolina - Charlotte, N = 61	Blue Haven, N = 11	C.W. Williams Community Health Center, N = 171	Cabarrus Victims Assistance Network, N = 31	Camino Community Center, N = 31	Campaign for Southern Equality, N = 31	Caramore Community, N = 41	Cardinal Innovations, N = 691	Care Ring, N = 131	Carolinas CARE Partnership, N = 91	Carolinas Medical Center - Carolinas HealthCare System, N = 21	Catherines House, N = 31	Catholic Charities - Charlotte Regional Office, N = 201	Center for Community Transitions, N = 51	Center for Family Violence Prevention, N = 11	Charles George Veterans Affairs Medical Center, N = 11	Charlotte Area Fund, N = 1201	Charlotte Area Transit System, N = 171	Charlotte Berean Seventh Day Adventist Church Community Center, N = 441	Charlotte Center for Legal Advocacy, N = 201	Charlotte Community Health Clinic, N = 71	Charlotte Family Housing, N = 2611	Charlotte Rescue Mission, N = 91	Charlotte Vet Center, N = 21	Charlotte Works, N = 21	Charlottetown Manor, N = 31	Child Care Resources, N = 201	Community Link, N = 5801	Community Shelter of Union County, N = 51	Community Support Services of Mecklenburg County, N = 4,4341	Cooperative Christian Ministry, N = 61	Coronavirus, N = 7011	Crisis Assistance Ministry, N = 1451	CriSyS, N = 21	Davidson Housing Coalition, N = 31	Debt Reduction Services, N = 21	Department of Public Health - Mecklenburg County, N = 131	Department of Social Services - Mecklenburg County, N = 1741	Derita Presbyterian Church child care, N = 31	Dilworth Soup Kitchen, N = 241	Disability Rights and Resources, N = 41	DreamKey Partners, N = 101	Dress for Success - Charlotte, N = 111	Durham Crisis Response Center, N = 11	Easterseals UCP, N = 21	EnergyUnited, N = 361	Esther House, N = 21	Family Promise of Gaston County, N = 51	Family Promise of Wake County, N = 21	FeedNC, N = 171	Fifth Street Ministries, N = 71	Florence Crittenton Services, N = 231	Friendship Trays, N = 51	Gaston Community Action, N = 11	Goodwill - Southern Piedmont, N = 371	Goodwill Career Connections Center - Catawba County Center, N = 61	Government Services - City of Charlotte, N = 91	Government Services - Mecklenburg County, N = 61	Government Services - Scotland County, N = 21	Grocery Worker's Relief Fund, N = 31	Habitat for Humanity - Cabarrus County, N = 21	Habitat for Humanity - Charlotte Region, N = 31	Habitat for Humanity - Greater Matthews, N = 21	Habitat for Humanity - Our Towns, N = 11	Harvest Center of Charlotte, N = 1251	Helping Hand Mission, N = 21	HIV/AIDS Hotline, N = 21	Homes of Hope, N = 21	Hope Haven, N = 21	Hope House Foundation, N = 1451	Hope Street Food Pantry, N = 171	Humane Society - Charlotte, N = 41	If My People Food Pantry, N = 261	Inlivian, N = 1561	Internal Initiatives, N = 11	Internal Revenue Service, N = 21	Jewish Community Services of South Florida, N = 21	Jewish Family Services of Greater Charlotte, N = 31	Latin American Coalition, N = 31	Leading Into New Communities, N = 141	Legal Aid of North Carolina, N = 501	Leukemia and Lymphoma Society, N = 21	Liberty Baptist Church Food Pantry, N = 751	Lifeline, N = 41	LIFESPAN, N = 21	Loaves and Fishes, N = 691	Lois Lodge Maternity Home, N = 31	Lutheran Services Carolinas, N = 11	Matthews Free Medical Clinic, N = 41	Matthews Help Center, N = 141	Mayfield Memorial Apartments, N = 851	McDowell Street Center for Family Law, N = 111	McLeod Addictive Disease Center, N = 91	Mecklenburg County Senior Center, N = 31	MECKLINK Behavioral Healthcare, N = 21	Mens Shelter of Charlotte, N = 3841	Metrolina Association for the Blind, N = 11	MiraVia, N = 171	Mission Hospital, N = 21	Mother of Mercy Catholic Church, N = 31	Mount Olive Presbyterian Church, N = 501	My Sisters Success of North Carolina, N = 11	National Domestic Violence Hotline, N = 1611	Navy-Marine Corps Relief Society, N = 31	NC MedAssist, N = 151	NCHousingSearch.org, N = 7111	NCWorks Career Center - Mecklenburg County, N = 721	NeedyMeds, N = 21	New Outreach Christian Center, N = 331	North Carolina Back@Home Program, N = 231	North Carolina Baptist Aging Ministry, N = 51	North Carolina Bar Association, N = 31	North Carolina Continuum of Care, N = 11,3431	North Carolina Council of the Blind, N = 51	North Carolina Department of Commerce, N = 21	North Carolina Division of Health Service Regulation, N = 41	North Carolina Division of Motor Vehicles, N = 311	North Carolina Division of Motor Vehicles - Mecklenburg County, N = 21	North Carolina Division of Services for the Blind - Charlotte District, N = 31	North Carolina Division of Vocational Rehabilitation - Charlotte, N = 61	North Carolina Emergency Management, N = 21	North Carolina Governor, N = 11	North Carolina HOPE Program, N = 31	North Carolina Housing Finance Agency, N = 21	North Carolina Lions Club, N = 21	North Carolina Marketplace In-Person Assistance, N = 21	North Carolina Medicaid, N = 41	North Carolina Missions of Mercy, N = 41	North Carolina Operated Healthcare Facilities, N = 21	North Carolina Oxford Houses, N = 51	Novant Health - Michael Jordan Family Medical Clinic, N = 21	Open Door Ministries of High Point, N = 11	Operation Home Front, N = 41	P. K. Management, N = 21	Pinecrest Manor, N = 21	Pines at Carolina Place The, N = 41	Plaza Baptist Church Day Care, N = 31	Pregnancy Resource Center of Charlotte, N = 21	Presbyterian Healthcare, N = 51	Primary Health-Care of Charlotte P.A., N = 21	Professionals in Transition, N = 21	Project Outpour, N = 61	Prosperity Unlimited, N = 21	Queen City Worship Center, N = 451	RAIN, N = 71	RAMP Charlotte, N = 1621	Rape Abuse and Incest National Network, N = 31	REAL Crisis Intervention, N = 31	Rescue Missions Ministries, N = 11	Resident Relief Foundation, N = 51	Room at the Inn, N = 91	Rowan Helping Ministries, N = 61	Safe Alliance, N = 5941	Safe Harbor, N = 1941	Safe Haven of Person County, N = 21	Salisbury Rowan Community Action Agency, N = 31	Salvation Army - Cabarrus and Stanly Counties, N = 421	Salvation Army - Greater Charlotte, N = 3701	Salvation Army - Sandhills Region, N = 11	Salvation Army - Wake County, N = 21	Salvation Army Center of Hope - Gaston County, N = 251	School District - Charlotte-Mecklenburg, N = 21	Self-Help Credit Union - Charlotte, N = 51	Selwyn Ave. Presbyterian Church Child Care, N = 31	Servants Heart of Mint Hill, N = 81	Shalom Adonai Church of God, N = 211	Sharon Baptist Church Child Care, N = 31	Sharon Village Retirement Apartments, N = 31	SingleCare Prescription Card, N = 31	St. Paul Baptist Church Food Pantry, N = 481	Substance Abuse and Mental Health Services Administration, N = 21	Supportive Housing Communities, N = 1631	Syrenity House Empowered, N = 111	The Relatives, N = 251	Thrift United Methodist Church, N = 671	Toys for Tots, N = 31	Trans Lifeline, N = 31	Turning Point, N = 451	United States Centers for Disease Control and Prevention, N = 61	United States Department of Veterans Affairs, N = 81	United States Social Security Administration, N = 221	United Way Association of South Carolina, N = 61	United Way of North Carolina, N = 41	University Soup Kitchen, N = 21	Urban League of Central Carolinas, N = 111	Urban Ministry Center, N = 3591	Veterans Bridge Home, N = 421	Victory Christian Center, N = 781	YWCA - Central Carolinas, N = 721
GENDER
Female	3 (100%)	2 (100%)	2 (50%)	2 (100%)	0 (0%)	2 (100%)	11 (61%)	9 (82%)	3 (50%)	12 (31%)	6 (100%)	1 (100%)	6 (35%)	3 (100%)	3 (100%)	0 (0%)	2 (50%)	39 (59%)	9 (69%)	4 (44%)	0 (0%)	3 (100%)	14 (70%)	2 (40%)	1 (100%)	1 (100%)	56 (49%)	14 (82%)	28 (67%)	10 (50%)	5 (71%)	221 (86%)	2 (22%)	0 (0%)	2 (100%)	0 (0%)	18 (90%)	383 (67%)	3 (60%)	2,484 (57%)	6 (100%)	391 (67%)	82 (57%)	2 (100%)	1 (33%)	2 (100%)	3 (23%)	104 (60%)	3 (100%)	11 (46%)	2 (50%)	8 (80%)	11 (100%)	1 (100%)	0 (0%)	23 (64%)	2 (100%)	5 (100%)	2 (100%)	8 (47%)	7 (100%)	21 (91%)	2 (40%)	0 (0%)	13 (38%)	6 (100%)	9 (100%)	3 (50%)	0 (0%)	0 (0%)	2 (100%)	1 (100%)	0 (NA%)	1 (100%)	86 (69%)	2 (100%)	0 (0%)	2 (100%)	0 (0%)	134 (96%)	10 (59%)	2 (50%)	19 (79%)	123 (80%)	1 (100%)	0 (0%)	2 (100%)	2 (67%)	1 (33%)	0 (0%)	34 (68%)	2 (100%)	49 (67%)	0 (0%)	0 (0%)	56 (81%)	3 (100%)	0 (0%)	4 (100%)	11 (79%)	50 (62%)	9 (82%)	5 (56%)	0 (0%)	0 (0%)	20 (5.3%)	0 (0%)	17 (100%)	2 (100%)	3 (100%)	38 (79%)	1 (100%)	152 (94%)	1 (33%)	4 (31%)	479 (69%)	22 (32%)	0 (0%)	23 (70%)	16 (70%)	5 (100%)	3 (100%)	6,446 (58%)	3 (60%)	0 (0%)	0 (0%)	14 (45%)	0 (0%)	0 (0%)	2 (33%)	2 (100%)	1 (100%)	3 (100%)	2 (100%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	2 (100%)	1 (20%)	0 (0%)	0 (0%)	2 (50%)	2 (100%)	0 (0%)	4 (100%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	2 (33%)	2 (100%)	26 (58%)	0 (0%)	134 (83%)	0 (0%)	0 (0%)	0 (0%)	5 (100%)	7 (78%)	2 (33%)	522 (91%)	186 (98%)	2 (100%)	0 (0%)	31 (74%)	344 (94%)	0 (0%)	2 (100%)	19 (76%)	2 (100%)	5 (100%)	3 (100%)	8 (100%)	14 (74%)	3 (100%)	0 (0%)	0 (0%)	31 (67%)	2 (100%)	99 (61%)	11 (100%)	7 (28%)	46 (69%)	3 (100%)	0 (0%)	45 (100%)	6 (100%)	5 (62%)	12 (55%)	6 (100%)	2 (50%)	0 (0%)	5 (45%)	154 (45%)	10 (24%)	52 (67%)	69 (97%)
Male	0 (0%)	0 (0%)	2 (50%)	0 (0%)	2 (100%)	0 (0%)	7 (39%)	2 (18%)	3 (50%)	27 (69%)	0 (0%)	0 (0%)	11 (65%)	0 (0%)	0 (0%)	0 (0%)	2 (50%)	25 (38%)	4 (31%)	5 (56%)	2 (100%)	0 (0%)	6 (30%)	3 (60%)	0 (0%)	0 (0%)	59 (51%)	3 (18%)	14 (33%)	10 (50%)	2 (29%)	37 (14%)	5 (56%)	2 (100%)	0 (0%)	3 (100%)	2 (10%)	185 (32%)	2 (40%)	1,888 (43%)	0 (0%)	190 (32%)	59 (41%)	0 (0%)	2 (67%)	0 (0%)	10 (77%)	68 (40%)	0 (0%)	13 (54%)	2 (50%)	2 (20%)	0 (0%)	0 (0%)	2 (100%)	13 (36%)	0 (0%)	0 (0%)	0 (0%)	9 (53%)	0 (0%)	2 (8.7%)	3 (60%)	1 (100%)	21 (62%)	0 (0%)	0 (0%)	3 (50%)	2 (100%)	3 (100%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	38 (31%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (3.6%)	7 (41%)	2 (50%)	5 (21%)	30 (20%)	0 (0%)	2 (100%)	0 (0%)	1 (33%)	2 (67%)	14 (100%)	16 (32%)	0 (0%)	24 (33%)	4 (100%)	2 (100%)	13 (19%)	0 (0%)	1 (100%)	0 (0%)	3 (21%)	31 (38%)	2 (18%)	4 (44%)	3 (100%)	2 (100%)	354 (95%)	1 (100%)	0 (0%)	0 (0%)	0 (0%)	10 (21%)	0 (0%)	9 (5.6%)	2 (67%)	9 (69%)	217 (31%)	47 (68%)	2 (100%)	10 (30%)	7 (30%)	0 (0%)	0 (0%)	4,609 (41%)	2 (40%)	2 (100%)	4 (100%)	17 (55%)	2 (100%)	3 (100%)	4 (67%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	4 (100%)	4 (100%)	0 (0%)	4 (80%)	2 (100%)	1 (100%)	2 (50%)	0 (0%)	2 (100%)	0 (0%)	3 (100%)	0 (0%)	3 (100%)	2 (100%)	2 (100%)	4 (67%)	0 (0%)	14 (31%)	7 (100%)	28 (17%)	3 (100%)	0 (0%)	1 (100%)	0 (0%)	2 (22%)	4 (67%)	48 (8.4%)	3 (1.6%)	0 (0%)	3 (100%)	11 (26%)	20 (5.5%)	1 (100%)	0 (0%)	6 (24%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (26%)	0 (0%)	3 (100%)	3 (100%)	15 (33%)	0 (0%)	64 (39%)	0 (0%)	18 (72%)	21 (31%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (38%)	8 (36%)	0 (0%)	2 (50%)	2 (100%)	6 (55%)	190 (55%)	32 (76%)	26 (33%)	2 (2.8%)
Refused to Disclose	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (3.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (<0.1%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	20 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Transgender	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (22%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	7 (1.2%)	0 (0%)	18 (0.4%)	0 (0%)	3 (0.5%)	3 (2.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	39 (0.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (11%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	2 (9.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (0.6%)	0 (0%)	0 (0%)	0 (0%)
DISABILITY_STATUS
No	1 (33%)	2 (100%)	3 (75%)	2 (100%)	2 (100%)	0 (0%)	11 (61%)	11 (100%)	3 (50%)	20 (53%)	2 (50%)	0 (0%)	9 (53%)	2 (67%)	3 (100%)	3 (100%)	4 (100%)	35 (53%)	7 (54%)	7 (78%)	2 (100%)	3 (100%)	19 (95%)	5 (100%)	0 (NA%)	1 (100%)	87 (74%)	7 (41%)	27 (64%)	9 (45%)	5 (71%)	204 (79%)	7 (78%)	2 (100%)	2 (100%)	0 (0%)	12 (60%)	375 (65%)	4 (80%)	2,928 (67%)	6 (100%)	437 (75%)	92 (63%)	2 (100%)	1 (33%)	2 (100%)	9 (69%)	80 (47%)	0 (0%)	17 (71%)	2 (50%)	8 (80%)	6 (55%)	0 (0%)	0 (0%)	22 (61%)	1 (100%)	5 (100%)	2 (100%)	9 (53%)	7 (100%)	16 (70%)	0 (0%)	1 (100%)	22 (65%)	4 (67%)	9 (100%)	3 (50%)	0 (0%)	0 (0%)	2 (100%)	1 (100%)	0 (NA%)	1 (100%)	101 (81%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	108 (76%)	12 (71%)	4 (100%)	13 (54%)	97 (62%)	1 (100%)	2 (100%)	2 (100%)	2 (67%)	1 (33%)	6 (43%)	27 (54%)	2 (100%)	45 (62%)	4 (100%)	0 (0%)	52 (75%)	3 (100%)	1 (100%)	2 (50%)	13 (93%)	18 (22%)	7 (64%)	7 (78%)	0 (0%)	0 (0%)	267 (72%)	1 (100%)	17 (100%)	2 (100%)	1 (33%)	27 (56%)	1 (100%)	114 (72%)	3 (100%)	2 (15%)	461 (66%)	53 (77%)	0 (0%)	23 (70%)	14 (61%)	3 (60%)	0 (0%)	7,503 (68%)	0 (0%)	2 (100%)	2 (50%)	15 (48%)	2 (100%)	0 (0%)	2 (33%)	1 (50%)	1 (100%)	3 (100%)	0 (0%)	2 (100%)	2 (100%)	2 (50%)	2 (50%)	0 (0%)	5 (100%)	0 (0%)	1 (100%)	0 (0%)	0 (0%)	2 (100%)	2 (50%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (67%)	0 (0%)	28 (62%)	3 (43%)	123 (76%)	3 (100%)	3 (100%)	1 (100%)	1 (20%)	9 (100%)	4 (67%)	418 (76%)	152 (79%)	1 (50%)	3 (100%)	28 (67%)	257 (71%)	1 (100%)	2 (100%)	21 (84%)	0 (0%)	5 (100%)	0 (0%)	4 (50%)	11 (58%)	0 (0%)	0 (0%)	0 (0%)	26 (57%)	2 (100%)	95 (58%)	9 (82%)	18 (72%)	39 (58%)	3 (100%)	3 (100%)	23 (61%)	4 (67%)	3 (38%)	10 (45%)	4 (67%)	3 (100%)	0 (0%)	5 (45%)	229 (66%)	28 (67%)	40 (51%)	52 (72%)
Not Collected	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (3.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	1 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	21 (0.5%)	0 (0%)	1 (0.2%)	1 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (2.9%)	0 (0%)	2 (8.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (12%)	0 (0%)	2 (8.7%)	0 (0%)	0 (0%)	2 (5.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (67%)	0 (0%)	2 (4.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (18%)	0 (0%)	0 (0%)	0 (0%)	4 (1.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (0.6%)	2 (2.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	409 (3.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	2 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	11 (2.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (5.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	9 (2.6%)	0 (0%)	0 (0%)	0 (0%)
Refused to Disclose	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (5.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.2%)	0 (0%)	10 (0.2%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	25 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (0.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Yes	2 (67%)	0 (0%)	1 (25%)	0 (0%)	0 (0%)	2 (100%)	7 (39%)	0 (0%)	3 (50%)	18 (47%)	2 (50%)	1 (100%)	8 (47%)	1 (33%)	0 (0%)	0 (0%)	0 (0%)	29 (44%)	6 (46%)	2 (22%)	0 (0%)	0 (0%)	1 (5.0%)	0 (0%)	0 (NA%)	0 (0%)	30 (25%)	10 (59%)	15 (36%)	10 (50%)	2 (29%)	52 (20%)	2 (22%)	0 (0%)	0 (0%)	3 (100%)	8 (40%)	197 (34%)	1 (20%)	1,433 (33%)	0 (0%)	142 (24%)	52 (36%)	0 (0%)	2 (67%)	0 (0%)	4 (31%)	87 (51%)	3 (100%)	5 (21%)	2 (50%)	2 (20%)	5 (45%)	1 (100%)	2 (100%)	14 (39%)	0 (0%)	0 (0%)	0 (0%)	6 (35%)	0 (0%)	5 (22%)	5 (100%)	0 (0%)	10 (29%)	2 (33%)	0 (0%)	3 (50%)	2 (100%)	3 (100%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	23 (19%)	0 (0%)	2 (100%)	2 (100%)	2 (100%)	35 (24%)	5 (29%)	0 (0%)	11 (46%)	59 (38%)	0 (0%)	0 (0%)	0 (0%)	1 (33%)	0 (0%)	8 (57%)	20 (40%)	0 (0%)	28 (38%)	0 (0%)	2 (100%)	17 (25%)	0 (0%)	0 (0%)	2 (50%)	1 (7.1%)	63 (78%)	2 (18%)	2 (22%)	3 (100%)	2 (100%)	102 (27%)	0 (0%)	0 (0%)	0 (0%)	2 (67%)	21 (44%)	0 (0%)	44 (28%)	0 (0%)	11 (85%)	235 (34%)	14 (20%)	2 (100%)	10 (30%)	9 (39%)	2 (40%)	3 (100%)	3,170 (29%)	5 (100%)	0 (0%)	2 (50%)	16 (52%)	0 (0%)	3 (100%)	4 (67%)	1 (50%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	2 (50%)	2 (50%)	2 (100%)	0 (0%)	2 (100%)	0 (0%)	4 (100%)	2 (100%)	0 (0%)	2 (50%)	0 (0%)	2 (100%)	3 (100%)	2 (100%)	0 (0%)	0 (0%)	2 (100%)	17 (38%)	4 (57%)	39 (24%)	0 (0%)	0 (0%)	0 (0%)	4 (80%)	0 (0%)	2 (33%)	119 (22%)	40 (21%)	1 (50%)	0 (0%)	14 (33%)	103 (28%)	0 (0%)	0 (0%)	4 (16%)	2 (100%)	0 (0%)	3 (100%)	4 (50%)	8 (42%)	3 (100%)	3 (100%)	3 (100%)	20 (43%)	0 (0%)	68 (42%)	2 (18%)	7 (28%)	28 (42%)	0 (0%)	0 (0%)	13 (34%)	2 (33%)	5 (62%)	12 (55%)	2 (33%)	0 (0%)	0 (0%)	6 (55%)	108 (31%)	14 (33%)	38 (49%)	20 (28%)
MILITARY_STATUS
Active	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (<0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	14 (0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
No	3 (100%)	2 (100%)	4 (100%)	2 (100%)	2 (100%)	2 (100%)	15 (83%)	8 (73%)	6 (100%)	8 (21%)	2 (50%)	1 (100%)	17 (100%)	3 (100%)	3 (100%)	3 (100%)	4 (100%)	57 (92%)	13 (100%)	9 (100%)	2 (100%)	2 (67%)	18 (90%)	5 (100%)	0 (NA%)	0 (0%)	114 (97%)	12 (80%)	40 (95%)	17 (89%)	7 (100%)	243 (95%)	7 (100%)	0 (0%)	2 (100%)	0 (0%)	20 (100%)	535 (94%)	5 (100%)	4,107 (94%)	6 (100%)	559 (96%)	143 (99%)	2 (100%)	3 (100%)	2 (100%)	13 (100%)	159 (95%)	3 (100%)	22 (92%)	4 (100%)	8 (80%)	11 (100%)	1 (100%)	2 (100%)	32 (89%)	1 (100%)	5 (100%)	2 (100%)	11 (65%)	7 (100%)	20 (87%)	5 (100%)	1 (100%)	29 (85%)	4 (100%)	7 (78%)	3 (50%)	0 (0%)	3 (100%)	2 (100%)	1 (100%)	0 (NA%)	1 (100%)	123 (99%)	2 (100%)	0 (NA%)	2 (100%)	0 (NA%)	137 (96%)	17 (100%)	4 (100%)	24 (100%)	154 (99%)	1 (100%)	2 (100%)	2 (100%)	3 (100%)	1 (33%)	14 (100%)	42 (86%)	2 (100%)	66 (90%)	4 (100%)	2 (100%)	60 (87%)	0 (0%)	1 (100%)	4 (100%)	14 (100%)	79 (98%)	9 (82%)	9 (100%)	3 (100%)	2 (100%)	340 (93%)	1 (100%)	14 (82%)	2 (100%)	3 (100%)	45 (94%)	1 (100%)	150 (95%)	0 (0%)	13 (100%)	641 (93%)	64 (93%)	2 (100%)	33 (100%)	21 (91%)	5 (100%)	3 (100%)	10,303 (94%)	5 (100%)	2 (100%)	4 (100%)	26 (84%)	2 (100%)	3 (100%)	4 (100%)	2 (100%)	1 (100%)	3 (100%)	2 (100%)	2 (100%)	2 (100%)	4 (100%)	4 (100%)	2 (100%)	5 (100%)	2 (100%)	0 (0%)	0 (0%)	2 (100%)	2 (100%)	4 (100%)	3 (100%)	2 (100%)	3 (100%)	2 (100%)	0 (0%)	4 (67%)	2 (100%)	40 (89%)	7 (100%)	159 (98%)	3 (100%)	3 (100%)	1 (100%)	5 (100%)	6 (67%)	4 (67%)	517 (95%)	192 (100%)	2 (100%)	3 (100%)	33 (79%)	341 (97%)	1 (100%)	2 (100%)	25 (100%)	2 (100%)	5 (100%)	3 (100%)	8 (100%)	19 (100%)	3 (100%)	0 (0%)	3 (100%)	43 (93%)	2 (100%)	155 (97%)	11 (100%)	23 (92%)	59 (88%)	3 (100%)	3 (100%)	37 (97%)	5 (83%)	0 (0%)	16 (80%)	4 (67%)	4 (100%)	0 (0%)	11 (100%)	315 (92%)	0 (0%)	78 (100%)	69 (97%)
Not Collected	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	9 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (4.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (18%)	0 (0%)	0 (0%)	0 (0%)	3 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	44 (0.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (0.9%)	0 (0%)	0 (0%)	0 (0%)	2 (4.8%)	5 (1.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.6%)	0 (0%)	0 (0%)	2 (10%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (1.5%)	0 (0%)	0 (0%)	0 (0%)
Refused to Disclose	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	8 (0.2%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (22%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	11 (0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Veteran	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (17%)	3 (27%)	0 (0%)	31 (79%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (8.1%)	0 (0%)	0 (0%)	0 (0%)	1 (33%)	2 (10%)	0 (0%)	0 (NA%)	1 (100%)	4 (3.4%)	3 (20%)	2 (4.8%)	2 (11%)	0 (0%)	10 (3.9%)	0 (0%)	2 (100%)	0 (0%)	3 (100%)	0 (0%)	32 (5.6%)	0 (0%)	245 (5.6%)	0 (0%)	22 (3.8%)	1 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	7 (4.2%)	0 (0%)	2 (8.3%)	0 (0%)	2 (20%)	0 (0%)	0 (0%)	0 (0%)	4 (11%)	0 (0%)	0 (0%)	0 (0%)	6 (35%)	0 (0%)	3 (13%)	0 (0%)	0 (0%)	5 (15%)	0 (0%)	0 (0%)	3 (50%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	1 (0.8%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)	6 (4.2%)	0 (0%)	0 (0%)	0 (0%)	2 (1.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (67%)	0 (0%)	5 (10%)	0 (0%)	7 (9.6%)	0 (0%)	0 (0%)	9 (13%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	2 (2.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	22 (6.0%)	0 (0%)	3 (18%)	0 (0%)	0 (0%)	3 (6.2%)	0 (0%)	8 (5.1%)	3 (100%)	0 (0%)	49 (7.1%)	5 (7.2%)	0 (0%)	0 (0%)	2 (8.7%)	0 (0%)	0 (0%)	602 (5.5%)	0 (0%)	0 (0%)	0 (0%)	5 (16%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (100%)	4 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	2 (33%)	0 (0%)	5 (11%)	0 (0%)	3 (1.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (33%)	2 (33%)	16 (2.9%)	0 (0%)	0 (0%)	0 (0%)	7 (17%)	7 (2.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	3 (6.5%)	0 (0%)	5 (3.1%)	0 (0%)	2 (8.0%)	8 (12%)	0 (0%)	0 (0%)	0 (0%)	1 (17%)	8 (100%)	2 (10%)	2 (33%)	0 (0%)	2 (100%)	0 (0%)	22 (6.4%)	42 (100%)	0 (0%)	2 (2.8%)
AGE
17 - younger	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (0.7%)	0 (0%)	7 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (20%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (2.1%)	0 (0%)	0 (0%)	0 (0%)	2 (1.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (0.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	35 (0.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.2%)	5 (2.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (20%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
18 - 59	3 (100%)	2 (100%)	4 (100%)	2 (100%)	2 (100%)	2 (100%)	15 (83%)	11 (100%)	0 (0%)	37 (97%)	2 (50%)	1 (100%)	16 (94%)	3 (100%)	3 (100%)	3 (100%)	4 (100%)	64 (97%)	11 (85%)	9 (100%)	2 (100%)	2 (67%)	20 (100%)	5 (100%)	0 (NA%)	1 (100%)	111 (94%)	12 (71%)	42 (100%)	19 (95%)	7 (100%)	249 (98%)	9 (100%)	2 (100%)	2 (100%)	0 (0%)	20 (100%)	529 (92%)	5 (100%)	4,031 (92%)	6 (100%)	536 (93%)	139 (96%)	2 (100%)	1 (33%)	2 (100%)	13 (100%)	149 (87%)	3 (100%)	20 (83%)	2 (50%)	8 (80%)	11 (100%)	1 (100%)	2 (100%)	33 (92%)	1 (100%)	5 (100%)	2 (100%)	15 (88%)	7 (100%)	23 (100%)	2 (40%)	1 (100%)	32 (94%)	6 (100%)	9 (100%)	6 (100%)	0 (0%)	3 (100%)	2 (100%)	1 (100%)	0 (NA%)	1 (100%)	118 (95%)	2 (100%)	2 (100%)	2 (100%)	2 (100%)	133 (93%)	17 (100%)	2 (50%)	22 (85%)	144 (92%)	0 (0%)	2 (100%)	2 (100%)	3 (100%)	1 (33%)	10 (71%)	36 (72%)	2 (100%)	69 (95%)	4 (100%)	2 (100%)	69 (100%)	3 (100%)	1 (100%)	4 (100%)	14 (100%)	51 (63%)	9 (82%)	7 (78%)	0 (0%)	2 (100%)	347 (93%)	0 (0%)	17 (100%)	2 (100%)	3 (100%)	48 (100%)	1 (100%)	154 (97%)	3 (100%)	11 (85%)	625 (89%)	64 (93%)	0 (0%)	33 (100%)	22 (96%)	0 (0%)	3 (100%)	10,153 (91%)	5 (100%)	2 (100%)	4 (100%)	26 (84%)	2 (100%)	3 (100%)	4 (67%)	2 (100%)	1 (100%)	0 (0%)	0 (0%)	2 (100%)	2 (100%)	2 (50%)	4 (100%)	2 (100%)	5 (100%)	2 (100%)	0 (0%)	3 (75%)	2 (100%)	0 (0%)	4 (100%)	3 (100%)	2 (100%)	0 (0%)	2 (100%)	0 (0%)	4 (67%)	0 (0%)	43 (96%)	7 (100%)	159 (98%)	3 (100%)	3 (100%)	1 (100%)	5 (100%)	9 (100%)	5 (83%)	525 (95%)	184 (96%)	2 (100%)	3 (100%)	41 (98%)	346 (95%)	1 (100%)	2 (100%)	23 (92%)	2 (100%)	5 (100%)	3 (100%)	6 (75%)	15 (79%)	3 (100%)	0 (0%)	3 (100%)	46 (96%)	2 (100%)	151 (93%)	11 (100%)	18 (72%)	63 (94%)	3 (100%)	3 (100%)	37 (97%)	6 (100%)	8 (100%)	18 (82%)	4 (67%)	3 (100%)	0 (0%)	11 (100%)	320 (93%)	34 (81%)	73 (94%)	64 (90%)
60 - 64	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.6%)	0 (0%)	0 (0%)	1 (5.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (3.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	2 (1.7%)	3 (18%)	0 (0%)	0 (0%)	0 (0%)	3 (1.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	22 (3.8%)	0 (0%)	191 (4.4%)	0 (0%)	18 (3.1%)	3 (2.1%)	0 (0%)	2 (67%)	0 (0%)	0 (0%)	13 (7.6%)	0 (0%)	2 (8.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (8.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (60%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	4 (3.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (2.8%)	0 (0%)	0 (0%)	2 (7.7%)	8 (5.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (29%)	7 (14%)	0 (0%)	4 (5.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	15 (19%)	0 (0%)	2 (22%)	0 (0%)	0 (0%)	24 (6.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1.3%)	0 (0%)	2 (15%)	35 (5.0%)	3 (4.3%)	2 (100%)	0 (0%)	0 (0%)	2 (40%)	0 (0%)	530 (4.8%)	0 (0%)	0 (0%)	0 (0%)	2 (6.5%)	0 (0%)	0 (0%)	2 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (67%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	2 (4.4%)	0 (0%)	1 (0.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (17%)	9 (1.6%)	0 (0%)	0 (0%)	0 (0%)	1 (2.4%)	14 (3.8%)	0 (0%)	0 (0%)	2 (8.0%)	0 (0%)	0 (0%)	0 (0%)	2 (25%)	4 (21%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	6 (3.7%)	0 (0%)	2 (8.0%)	4 (6.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (9.1%)	2 (33%)	0 (0%)	0 (0%)	0 (0%)	8 (2.3%)	5 (12%)	3 (3.8%)	7 (9.9%)
65 - older	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (17%)	0 (0%)	6 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (15%)	0 (0%)	0 (0%)	1 (33%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	5 (4.2%)	2 (12%)	0 (0%)	0 (0%)	0 (0%)	2 (0.8%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	18 (3.1%)	0 (0%)	141 (3.2%)	0 (0%)	16 (2.8%)	3 (2.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	8 (4.7%)	0 (0%)	0 (0%)	2 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	2 (1.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (2.1%)	0 (0%)	2 (50%)	2 (7.7%)	2 (1.3%)	1 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (8.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	15 (19%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	3 (0.8%)	1 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1.3%)	0 (0%)	0 (0%)	35 (5.0%)	0 (0%)	0 (0%)	0 (0%)	1 (4.3%)	3 (60%)	0 (0%)	317 (2.9%)	0 (0%)	0 (0%)	0 (0%)	3 (9.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	2 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (25%)	0 (0%)	2 (100%)	0 (0%)	0 (0%)	0 (0%)	1 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	7 (1.3%)	3 (1.6%)	0 (0%)	0 (0%)	0 (0%)	3 (0.8%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (100%)	0 (0%)	2 (4.2%)	0 (0%)	6 (3.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (9.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	11 (3.2%)	3 (7.1%)	2 (2.6%)	0 (0%)
Not Collected	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	9 (0.2%)	0 (0%)	4 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (1.2%)	0 (0%)	2 (8.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (12%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (5.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (67%)	0 (0%)	2 (4.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (18%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (2.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	46 (0.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	2 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	9 (1.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (100%)	0 (0%)	4 (1.2%)	0 (0%)	0 (0%)	0 (0%)
Refused to Disclose	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (5.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	11 (0.3%)	0 (0%)	1 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	19 (0.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	3 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
NEED
Housing	1 (33%)	1 (50%)	0 (0%)	1 (50%)	0 (0%)	0 (0%)	7 (39%)	4 (36%)	2 (33%)	21 (54%)	3 (50%)	1 (100%)	4 (24%)	3 (100%)	1 (33%)	1 (33%)	1 (25%)	21 (30%)	4 (31%)	2 (22%)	0 (0%)	2 (67%)	5 (25%)	2 (40%)	1 (100%)	1 (100%)	34 (28%)	4 (24%)	13 (30%)	5 (25%)	2 (29%)	160 (61%)	0 (0%)	0 (0%)	0 (0%)	1 (33%)	6 (30%)	287 (49%)	4 (80%)	3,994 (90%)	3 (50%)	365 (52%)	63 (43%)	1 (50%)	2 (67%)	1 (50%)	4 (31%)	50 (29%)	1 (33%)	6 (25%)	1 (25%)	5 (50%)	3 (27%)	1 (100%)	1 (50%)	6 (17%)	2 (100%)	3 (60%)	1 (50%)	4 (24%)	5 (71%)	6 (26%)	2 (40%)	0 (0%)	10 (27%)	0 (0%)	3 (33%)	2 (33%)	1 (50%)	1 (33%)	2 (100%)	2 (67%)	1 (50%)	1 (100%)	62 (50%)	1 (50%)	0 (0%)	1 (50%)	0 (0%)	74 (51%)	6 (35%)	2 (50%)	5 (19%)	84 (54%)	0 (0%)	0 (0%)	1 (50%)	1 (33%)	1 (33%)	3 (21%)	14 (28%)	0 (0%)	19 (25%)	0 (0%)	1 (50%)	17 (25%)	1 (33%)	1 (100%)	2 (50%)	6 (43%)	50 (59%)	3 (27%)	1 (11%)	1 (33%)	0 (0%)	296 (77%)	0 (0%)	4 (24%)	1 (50%)	2 (67%)	12 (24%)	0 (0%)	61 (38%)	2 (67%)	4 (27%)	406 (57%)	18 (25%)	1 (50%)	10 (30%)	21 (91%)	1 (20%)	1 (33%)	2,074 (18%)	1 (20%)	0 (0%)	2 (50%)	7 (23%)	0 (0%)	1 (33%)	1 (17%)	0 (0%)	0 (0%)	1 (33%)	1 (50%)	1 (50%)	1 (50%)	1 (25%)	1 (25%)	0 (0%)	0 (0%)	1 (50%)	1 (100%)	3 (75%)	1 (50%)	1 (50%)	2 (50%)	1 (33%)	1 (50%)	2 (40%)	1 (50%)	1 (50%)	1 (17%)	1 (50%)	10 (22%)	2 (29%)	90 (56%)	1 (33%)	1 (33%)	1 (100%)	3 (60%)	4 (44%)	4 (67%)	397 (67%)	93 (48%)	2 (100%)	1 (33%)	28 (67%)	276 (75%)	1 (100%)	1 (50%)	12 (48%)	1 (50%)	2 (40%)	1 (33%)	0 (0%)	4 (19%)	1 (33%)	1 (33%)	1 (33%)	11 (23%)	1 (50%)	88 (54%)	6 (55%)	12 (48%)	18 (27%)	1 (33%)	1 (33%)	23 (51%)	5 (83%)	3 (38%)	5 (23%)	2 (33%)	0 (0%)	1 (50%)	2 (18%)	190 (53%)	8 (19%)	17 (22%)	42 (58%)
Information Services	2 (67%)	1 (50%)	3 (75%)	0 (0%)	1 (50%)	1 (50%)	3 (17%)	2 (18%)	2 (33%)	15 (38%)	0 (0%)	0 (0%)	5 (29%)	0 (0%)	1 (33%)	1 (33%)	1 (25%)	13 (19%)	3 (23%)	3 (33%)	1 (50%)	1 (33%)	6 (30%)	1 (20%)	0 (0%)	0 (0%)	25 (21%)	5 (29%)	10 (23%)	6 (30%)	2 (29%)	87 (33%)	4 (44%)	1 (50%)	1 (50%)	1 (33%)	6 (30%)	216 (37%)	1 (20%)	272 (6.1%)	3 (50%)	177 (25%)	39 (27%)	0 (0%)	0 (0%)	0 (0%)	4 (31%)	41 (24%)	1 (33%)	7 (29%)	1 (25%)	4 (40%)	4 (36%)	0 (0%)	0 (0%)	10 (28%)	0 (0%)	2 (40%)	1 (50%)	5 (29%)	1 (14%)	6 (26%)	1 (20%)	0 (0%)	7 (19%)	3 (50%)	4 (44%)	2 (33%)	0 (0%)	1 (33%)	0 (0%)	1 (33%)	1 (50%)	0 (0%)	52 (42%)	1 (50%)	1 (50%)	1 (50%)	1 (50%)	58 (40%)	3 (18%)	0 (0%)	9 (35%)	58 (37%)	1 (100%)	1 (50%)	1 (50%)	0 (0%)	0 (0%)	4 (29%)	13 (26%)	1 (50%)	22 (29%)	2 (50%)	0 (0%)	22 (32%)	1 (33%)	0 (0%)	0 (0%)	4 (29%)	29 (34%)	3 (27%)	3 (33%)	1 (33%)	1 (50%)	62 (16%)	0 (0%)	6 (35%)	0 (0%)	0 (0%)	16 (32%)	0 (0%)	15 (9.3%)	0 (0%)	4 (27%)	232 (33%)	20 (28%)	0 (0%)	6 (18%)	0 (0%)	2 (40%)	1 (33%)	8,821 (78%)	2 (40%)	1 (50%)	2 (50%)	10 (32%)	1 (50%)	1 (33%)	2 (33%)	2 (100%)	0 (0%)	1 (33%)	1 (50%)	0 (0%)	0 (0%)	1 (25%)	1 (25%)	1 (50%)	2 (40%)	0 (0%)	0 (0%)	1 (25%)	1 (50%)	1 (50%)	2 (50%)	1 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (33%)	1 (50%)	17 (38%)	2 (29%)	53 (33%)	1 (33%)	1 (33%)	0 (0%)	0 (0%)	1 (11%)	2 (33%)	92 (15%)	84 (43%)	0 (0%)	1 (33%)	13 (31%)	69 (19%)	0 (0%)	1 (50%)	12 (48%)	0 (0%)	1 (20%)	1 (33%)	4 (50%)	7 (33%)	1 (33%)	1 (33%)	1 (33%)	17 (35%)	0 (0%)	65 (40%)	3 (27%)	10 (40%)	20 (30%)	0 (0%)	1 (33%)	6 (13%)	0 (0%)	2 (25%)	6 (27%)	3 (50%)	4 (100%)	0 (0%)	3 (27%)	105 (29%)	19 (45%)	26 (33%)	23 (32%)
Others	0 (0%)	0 (0%)	1 (25%)	1 (50%)	1 (50%)	1 (50%)	8 (44%)	5 (45%)	2 (33%)	3 (7.7%)	3 (50%)	0 (0%)	8 (47%)	0 (0%)	1 (33%)	1 (33%)	2 (50%)	35 (51%)	6 (46%)	4 (44%)	1 (50%)	0 (0%)	9 (45%)	2 (40%)	0 (0%)	0 (0%)	61 (51%)	8 (47%)	21 (48%)	9 (45%)	3 (43%)	14 (5.4%)	5 (56%)	1 (50%)	1 (50%)	1 (33%)	8 (40%)	77 (13%)	0 (0%)	168 (3.8%)	0 (0%)	159 (23%)	43 (30%)	1 (50%)	1 (33%)	1 (50%)	5 (38%)	83 (48%)	1 (33%)	11 (46%)	2 (50%)	1 (10%)	4 (36%)	0 (0%)	1 (50%)	20 (56%)	0 (0%)	0 (0%)	0 (0%)	8 (47%)	1 (14%)	11 (48%)	2 (40%)	1 (100%)	20 (54%)	3 (50%)	2 (22%)	2 (33%)	1 (50%)	1 (33%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	11 (8.8%)	0 (0%)	1 (50%)	0 (0%)	1 (50%)	13 (9.0%)	8 (47%)	2 (50%)	12 (46%)	14 (9.0%)	0 (0%)	1 (50%)	0 (0%)	2 (67%)	2 (67%)	7 (50%)	23 (46%)	1 (50%)	34 (45%)	2 (50%)	1 (50%)	30 (43%)	1 (33%)	0 (0%)	2 (50%)	4 (29%)	6 (7.1%)	5 (45%)	5 (56%)	1 (33%)	1 (50%)	26 (6.8%)	1 (100%)	7 (41%)	1 (50%)	1 (33%)	22 (44%)	1 (100%)	85 (53%)	1 (33%)	7 (47%)	73 (10%)	34 (47%)	1 (50%)	17 (52%)	2 (8.7%)	2 (40%)	1 (33%)	448 (3.9%)	2 (40%)	1 (50%)	0 (0%)	14 (45%)	1 (50%)	1 (33%)	3 (50%)	0 (0%)	1 (100%)	1 (33%)	0 (0%)	1 (50%)	1 (50%)	2 (50%)	2 (50%)	1 (50%)	3 (60%)	1 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (33%)	1 (50%)	3 (60%)	1 (50%)	1 (50%)	3 (50%)	0 (0%)	18 (40%)	3 (43%)	19 (12%)	1 (33%)	1 (33%)	0 (0%)	2 (40%)	4 (44%)	0 (0%)	105 (18%)	17 (8.8%)	0 (0%)	1 (33%)	1 (2.4%)	25 (6.8%)	0 (0%)	0 (0%)	1 (4.0%)	1 (50%)	2 (40%)	1 (33%)	4 (50%)	10 (48%)	1 (33%)	1 (33%)	1 (33%)	20 (42%)	1 (50%)	10 (6.1%)	2 (18%)	3 (12%)	29 (43%)	2 (67%)	1 (33%)	16 (36%)	1 (17%)	3 (38%)	11 (50%)	1 (17%)	0 (0%)	1 (50%)	6 (55%)	64 (18%)	15 (36%)	35 (45%)	7 (9.7%)
1 n (%)

#write.csv(q2,"../Data/Output/Q2.csv",row.names = F)